Abstract
BackgroundOrganisms utilize a multitude of mechanisms for responding to changing environmental conditions, maintaining their functional homeostasis and to overcome stress situations. One of the most important mechanisms is transcriptional gene regulation. In-depth study of the transcriptional gene regulatory network can lead to various practical applications, creating a greater understanding of how organisms control their cellular behavior.DescriptionIn this work, we present a new database, CMRegNet for the gene regulatory networks of Corynebacterium glutamicum ATCC 13032 and Mycobacterium tuberculosis H37Rv. We furthermore transferred the known networks of these model organisms to 18 other non-model but phylogenetically close species (target organisms) of the CMNR group. In comparison to other network transfers, for the first time we utilized two model organisms resulting into a more diverse and complete network of the target organisms.ConclusionCMRegNet provides easy access to a total of 3,103 known regulations in C. glutamicum ATCC 13032 and M. tuberculosis H37Rv and to 38,940 evolutionary conserved interactions for 18 non-model species of the CMNR group. This makes CMRegNet to date the most comprehensive database of regulatory interactions of CMNR bacteria. The content of CMRegNet is publicly available online via a web interface found at http://lgcm.icb.ufmg.br/cmregnet.
Highlights
Organisms utilize a multitude of mechanisms for responding to changing environmental conditions, maintaining their functional homeostasis and to overcome stress situations
The content of CMRegNet is publicly available online via a web interface found at http://lgcm.icb.ufmg.br/cmregnet
The ever growing number of completed genome sequencing projects has allowed for the extensive use of computational approaches for comparative genomics identifying potential transcriptional regulatory networks and key elements, such as transcription factor binding sites (TFBSs) [1]
Summary
Organisms utilize a multitude of mechanisms for responding to changing environmental conditions, maintaining their functional homeostasis and to overcome stress situations. The ever growing number of completed genome sequencing projects has allowed for the extensive use of computational approaches for comparative genomics identifying potential transcriptional regulatory networks and key elements, such as transcription factor binding sites (TFBSs) [1]. These studies primarily focus on analyzing and describing regulatory elements that have been previously identified in model organisms and how this information may be applicable to organisms that have yet to be characterized. The comparative analysis of regulators combined with other genomic-context analysis techniques significantly improves the quality and accuracy of the functional It is currently not possible, to decipher a complete regulatory network, even for a model organism.
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